



'''simple docstring'''




import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			A_      :		Dict      =	[
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(_lowerCamelCase		, _lowerCamelCase       )



def       a     (					lowerCamelCase__       ):
			'''simple docstring'''





			A_,       A_      :		int      =	emb.weight.shape
			A_      :		int      =	nn.Linear(_lowerCamelCase		, _lowerCamelCase		, bias=_lowerCamelCase       )
			A_      :		List[str]      =	emb.weight.data
			return lin_layer



def       a     (					lowerCamelCase__		, lowerCamelCase__=None       ):
			'''simple docstring'''





			A_      :		Optional[int]      =	{}
			for old_key in state_dict.keys():
						A_      :		Tuple      =	old_key
						if "moe_layer.experts." in key:
									if expert_idx is not None:
												A_      :		int      =	key.replace("""moe_layer.experts.0"""		, f'ffn.experts.expert_{expert_idx}'       )
									else:
												A_      :		Tuple      =	key.replace("""moe_layer.experts."""		, """ffn.experts.expert_"""       )
						if "gate" in key:
									A_      :		Tuple      =	key.replace(""".moe_layer.gate.wg"""		, """.ffn.router.classifier"""       )
						if "fc2" and "experts" not in key:
									A_      :		List[Any]      =	key.replace(""".fc2."""		, """.ffn.fc2."""       )
						if "fc1" and "experts" not in key:
									A_      :		str      =	key.replace(""".fc1."""		, """.ffn.fc1."""       )
						if ".encoder_attn." in key:
									A_      :		Tuple      =	key.replace(""".encoder_attn."""		, """.cross_attention."""       )
						if "encoder_attn_layer_norm" in key:
									A_      :		str      =	key.replace("""encoder_attn_layer_norm"""		, """cross_attention_layer_norm"""       )
						if "final_layer_norm" in key:
									A_      :		int      =	key.replace("""final_layer_norm"""		, """ff_layer_norm"""       )
						A_      :		Optional[Any]      =	state_dict[old_key]
			return new_dict



def       a     (					lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__		, lowerCamelCase__ = WEIGHTS_NAME       ):
			'''simple docstring'''





			A_      :		str      =	[]
			A_      :		Optional[int]      =	0
			os.makedirs(_lowerCamelCase		, exist_ok=_lowerCamelCase       )

			for expert in range(_lowerCamelCase       ):
						A_      :		Optional[Any]      =	switch_checkpoint_path + f'-rank-{expert}.pt'
						if os.path.isfile(_lowerCamelCase       ):
									A_      :		Optional[int]      =	torch.load(_lowerCamelCase       )["""model"""]
									remove_ignore_keys_(_lowerCamelCase       )
									A_      :		List[Any]      =	rename_fairseq_keys(_lowerCamelCase		, _lowerCamelCase       )
									A_      :		Tuple      =	os.path.join(
									    _lowerCamelCase		, weights_name.replace(""".bin"""		, f'-{len(_lowerCamelCase       )+1:05d}-of-???.bin'       )       )
									torch.save(_lowerCamelCase		, _lowerCamelCase       )
									sharded_state_dicts.append(expert_state.keys()       )
									total_size += sum([value.numel() for key, value in expert_state.items()]       ) * dtype_byte_size(
									    expert_state[list(_lowerCamelCase       )[0]].dtype       )

						# Add the last block
			A_      :		Dict      =	os.path.join(_lowerCamelCase		, weights_name.replace(""".bin"""		, f'-{len(_lowerCamelCase       )+1:05d}-of-???.bin'       )       )
			A_      :		Tuple      =	torch.load(switch_checkpoint_path + """-shared.pt"""       )["""model"""]
			remove_ignore_keys_(_lowerCamelCase       )
			A_      :		List[Any]      =	rename_fairseq_keys(_lowerCamelCase		, _lowerCamelCase       )
			A_      :		Tuple      =	shared_weights["""decoder.embed_tokens.weight"""]
			sharded_state_dicts.append(shared_weights.keys()       )

			# If we only have the shared weights (dummy model/experts saved on the same file)
			if len(_lowerCamelCase       ) == 1:
						A_      :		Optional[Any]      =	os.path.join(_lowerCamelCase		, _lowerCamelCase       )
						torch.save(_lowerCamelCase		, _lowerCamelCase       )
						return {weights_name: sharded_state_dicts[0]}, None
			else:
						torch.save(_lowerCamelCase		, _lowerCamelCase       )
			# Otherwise, let's build the index
			A_      :		Dict      =	{}
			for idx, shard in enumerate(_lowerCamelCase       ):
						A_      :		Tuple      =	weights_name.replace(""".bin"""		, f'-{idx+1:05d}-of-{len(_lowerCamelCase       ):05d}.bin'       )
						A_      :		Union[str, Any]      =	os.path.join(_lowerCamelCase		, weights_name.replace(""".bin"""		, f'-{idx+1:05d}-of-???.bin'       )       )
						os.rename(_lowerCamelCase		, os.path.join(_lowerCamelCase		, _lowerCamelCase       )       )
						for key in shard:
									A_      :		Optional[Any]      =	shard_file

						# Add the metadata
			A_      :		Union[str, Any]      =	{"""total_size""": total_size}
			A_      :		str      =	{"""metadata""": metadata, """weight_map""": weight_map}

			with open(os.path.join(_lowerCamelCase		, _lowerCamelCase       )		, """w"""		, encoding="""utf-8"""       ) as f:
						A_      :		List[Any]      =	json.dumps(_lowerCamelCase		, indent=2		, sort_keys=_lowerCamelCase       ) + """\n"""
						f.write(_lowerCamelCase       )

			return metadata, index


if __name__ == "__main__":
						lowerCamelCase				:Optional[int]							       =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--nllb_moe_checkpoint_path''',
						    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
						    type=str,
						    required=False,
						    help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
						)
						parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
						    type=str,
						    required=False,
						    help='''Path to the output pytorch model.''',
						)
						lowerCamelCase				:Union[str, Any]							       =   parser.parse_args()
						lowerCamelCase				:List[str]							       =   shard_on_the_fly(
						    args.nllb_moe_checkpoint_path,
						    args.pytorch_dump_folder_path,
						    1_2_8,
						    args.dtype,
						)

						lowerCamelCase				:str							       =   NllbMoeConfig.from_pretrained(
						    '''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
						)
						config.save_pretrained(args.pytorch_dump_folder_path)
						lowerCamelCase				:Any							       =   NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
						print('''Done''')
						model.save_pretrained(args.pytorch_dump_folder_path)







"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




